Supplemental

model_data <- long_data %>% 
  select(sub_id, bfi_number, cand_pref, ingroup_ident, threat, 
         self, target_number_collapsed, target_bfi_value, 
         condition_order, covid1, covid2, covid3, target_group,
         ingroup_ident_c, self_c, threat_c, gender, ethnicity, political_ident) %>% 
  drop_na()

contrasts(model_data$target_group) <- contr.sum(2)
model_base <- lmer(target_bfi_value ~ self_c*target_number_collapsed + (self_c|sub_id), data = model_data)
mod_vartest_ident <- lmer(target_bfi_value ~ self_c*target_number_collapsed*ingroup_ident_c + (self_c |sub_id), data = model_data)
mod_vartest_threat <- lmer(target_bfi_value ~ self_c*target_number_collapsed*threat_c + (self_c |sub_id), data = model_data)
mod_vartest_order <- lmer(target_bfi_value ~ self_c*target_number_collapsed*condition_order + (self_c |sub_id), data = model_data)

Residuals

Instructions

# checking normality of conditional residuals
qqnorm(residuals(model_base), main="Q-Q plot for conditional residuals")

# checking the normality of the random effects (here random slope:
qqnorm(ranef(model_base)$sub_id$self_c,
       main="Q-Q plot for the self random effect")

 # random intercept
qqnorm(ranef(model_base)$sub_id$`(Intercept)`,
       main="Q-Q plot for the target random effect")

plot_model(model_base, type='diag')

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In-group identification

# checking normality of conditional residuals
qqnorm(residuals(mod_vartest_ident), main="Q-Q plot for conditional residuals")

# checking the normality of the random effects (here random slope:
qqnorm(ranef(mod_vartest_ident)$sub_id$self_c,
       main="Q-Q plot for the self random effect")

 # random intercept
qqnorm(ranef(mod_vartest_ident)$sub_id$`(Intercept)`,
       main="Q-Q plot for the intercept")

plot_model(mod_vartest_ident, type='diag')

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Group-level threat

# checking normality of conditional residuals
qqnorm(residuals(mod_vartest_threat), main="Q-Q plot for conditional residuals")

# checking the normality of the random effects (here random slope:
qqnorm(ranef(mod_vartest_threat)$sub_id$self_c,
       main="Q-Q plot for the self random effect")

 # random intercept
qqnorm(ranef(mod_vartest_threat)$sub_id$`(Intercept)`,
       main="Q-Q plot for the intercept")

plot_model(mod_vartest_threat, type='diag')

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Target order

# checking normality of conditional residuals
qqnorm(residuals(mod_vartest_order), main="Q-Q plot for conditional residuals")

# checking the normality of the random effects (here random slope:
qqnorm(ranef(mod_vartest_order)$sub_id$self_c,
       main="Q-Q plot for the self random effect")

 # random intercept
qqnorm(ranef(mod_vartest_order)$sub_id$`(Intercept)`,
       main="Q-Q plot for the intercept")

plot_model(mod_vartest_order, type='diag')

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Multicolinearity

cor_bfi <- model_data %>% 
  select(self, target_bfi_value, threat, ingroup_ident) %>% 
  unique() %>% 
  rename("BFI: Self" = self,
         "BFI: Target" = target_bfi_value,
         "Group-level threat" = threat,
         "In-group identification" = ingroup_ident)

correlations_preds <- cor(cor_bfi)

corrplot(correlations_preds, 
         is.corr = TRUE, 
         #method = "number", 
         method = 'color',
         tl.cex = .85,
         tl.col = 'black',
         addgrid.col = 'white',
         addCoef.col = 'grey50',
         type = 'lower')

Normality - BFI

hist(long_data$self)

hist(long_data$target_bfi_value)